Incremental Pavement Distress Classification in UAV-Based Remote Sensing via Analytic Geometric Alignment.
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| Title: | Incremental Pavement Distress Classification in UAV-Based Remote Sensing via Analytic Geometric Alignment. |
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| Authors: | Wang, Quanziang1 (AUTHOR), Li, Xin2 (AUTHOR), Peng, Jiangjun3 (AUTHOR), Jia, Xixi4 (AUTHOR), Wang, Renzhen1 (AUTHOR) rzwang@xjtu.edu.cn |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1141. 21p. |
| Subjects: | Pavement testing, Drone aircraft, Edge computing, Machine learning, Loss functions (Statistics) |
| Abstract: | Highlights: What are the main findings? We propose a novel Analytic Geometric Alignment (AGA) framework for class-incremental pavement distress classification in UAV-based remote sensing, which innovatively integrates three key components: Subspace-Aware Analytic Initialization (SAI) to mathematically bridge the optimization gap for novel classes, a Decoupled Geometric Adapter (DGA) to decouple the global geometric aligment and local feature adaptation, and the Memory-Prioritized Regression (MPR) loss to enhance inter-class feature separability against complex UAV remote sensing backgrounds. On the UAV-PDD2023 dataset, AGA achieves state-of-the-art accuracy and stability in fine-grained pavement distress classification, which is also demonstrated on the auxiliary RDD2022 dataset. Notably, the model maintains robust performance even under extreme low-memory conditions (e.g., retaining only 100–200 exemplar samples), significantly alleviating catastrophic forgetting without incurring massive computational overhead. What are the implications of the main findings? The exceptional resource efficiency and anti-forgetting capability of AGA provide a highly deployable technical solution for continuous, long-term infrastructure monitoring on edge devices within UAV air–ground collaborative systems. This framework establishes a novel, data-efficient paradigm for processing streaming UAV imagery, offering robust support for dynamic remote sensing applications addressing critical challenges such as memory constraints, evolving target categories, and background interference. Automated pavement distress classification using high-resolution Unmanned Aerial Vehicle (UAV) imagery is pivotal for intelligent transportation systems. However, long-term UAV monitoring faces a continuous stream of evolving distress types and changing remote sensing background textures, necessitating Class-Incremental Learning (CIL) capabilities. Existing methods struggle to balance stability and plasticity, especially under the severe storage limitations typical of local edge stations in air–ground collaborative systems. This data scarcity leads to catastrophic forgetting and confusion among fine-grained distress categories. To address these challenges, we propose a data-efficient approach named Analytic Geometric Alignment (AGA). Our framework mainly consists of three key components. First, to overcome the optimization gap between the feature extractor and the fixed geometric target, we introduce a Subspace-Aware Analytic Initialization (SAI) that computes a closed-form projection to instantly align the feature subspace with the ETF manifold before each task training. Second, on this aligned basis, a Decoupled Geometric Adapter (DGA) is incorporated to facilitate continuous non-linear adaptation to complex aerial textures. Finally, for stable incremental training, we design a Memory-Prioritized Regression (MPR) loss to enforce tighter geometric constraints on replay samples, significantly enhancing model stability. Extensive experiments on the UAV-PDD2023 dataset demonstrate that AGA significantly outperforms state-of-the-art methods, showcasing excellent robustness and data efficiency. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We propose a novel Analytic Geometric Alignment (AGA) framework for class-incremental pavement distress classification in UAV-based remote sensing, which innovatively integrates three key components: Subspace-Aware Analytic Initialization (SAI) to mathematically bridge the optimization gap for novel classes, a Decoupled Geometric Adapter (DGA) to decouple the global geometric aligment and local feature adaptation, and the Memory-Prioritized Regression (MPR) loss to enhance inter-class feature separability against complex UAV remote sensing backgrounds. On the UAV-PDD2023 dataset, AGA achieves state-of-the-art accuracy and stability in fine-grained pavement distress classification, which is also demonstrated on the auxiliary RDD2022 dataset. Notably, the model maintains robust performance even under extreme low-memory conditions (e.g., retaining only 100–200 exemplar samples), significantly alleviating catastrophic forgetting without incurring massive computational overhead. What are the implications of the main findings? The exceptional resource efficiency and anti-forgetting capability of AGA provide a highly deployable technical solution for continuous, long-term infrastructure monitoring on edge devices within UAV air–ground collaborative systems. This framework establishes a novel, data-efficient paradigm for processing streaming UAV imagery, offering robust support for dynamic remote sensing applications addressing critical challenges such as memory constraints, evolving target categories, and background interference. Automated pavement distress classification using high-resolution Unmanned Aerial Vehicle (UAV) imagery is pivotal for intelligent transportation systems. However, long-term UAV monitoring faces a continuous stream of evolving distress types and changing remote sensing background textures, necessitating Class-Incremental Learning (CIL) capabilities. Existing methods struggle to balance stability and plasticity, especially under the severe storage limitations typical of local edge stations in air–ground collaborative systems. This data scarcity leads to catastrophic forgetting and confusion among fine-grained distress categories. To address these challenges, we propose a data-efficient approach named Analytic Geometric Alignment (AGA). Our framework mainly consists of three key components. First, to overcome the optimization gap between the feature extractor and the fixed geometric target, we introduce a Subspace-Aware Analytic Initialization (SAI) that computes a closed-form projection to instantly align the feature subspace with the ETF manifold before each task training. Second, on this aligned basis, a Decoupled Geometric Adapter (DGA) is incorporated to facilitate continuous non-linear adaptation to complex aerial textures. Finally, for stable incremental training, we design a Memory-Prioritized Regression (MPR) loss to enforce tighter geometric constraints on replay samples, significantly enhancing model stability. Extensive experiments on the UAV-PDD2023 dataset demonstrate that AGA significantly outperforms state-of-the-art methods, showcasing excellent robustness and data efficiency. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081141 |
